1. Field of the Disclosure
Aspects of the present disclosure relate generally to wireless communication techniques, and more particularly to an equalization system and method alternately implementing different data detection techniques such as, for example, maximum likelihood and zero forcing.
2. Description of Related Art
Recently, the wireless communication industry has demonstrated an increasing interest in multiple input, multiple output (MIMO) systems. For example, MIMO beamforming systems have been considered in standards promulgated by the Institute of Electrical and Electronics Engineers (IEEE), most notably the IEEE 802.11n standard for wireless local area networks (WLANs). One advantage of beamforming methodologies is that they can increase the data rate between networked devices without the attendant increase in transmit power or bandwidth that would be necessary to achieve a similar data rate in MIMO systems without beamforming. Spatial multiplexing techniques for MIMO systems have also been considered in several standards such as the IEEE 802.11a/g standard. One advantage of spatial multiplexing is that it can increase channel capacity at high signal to noise ratios (SNRs).
One relatively popular MIMO equalization approach uses maximum likelihood (ML) data detection techniques. Generally, ML detection is considered optimal to the extent that it exhaustively searches all the distances between a received data vector and the universe of possible data vectors in the constellation to identify an optimal point or data stream, i.e., the data received on multiple dimensions are considered collectively. In many instances, an exhaustive search ML approach may be too complex to be practical, so many systems employ a limited ML approach constrained by hardware or processing bandwidth limitations. Zero forcing (ZF) techniques, on the other hand, forcibly decouple the data received on each dimension (e.g., by zeroing out inter-antenna interference), then detect or decode each received data stream individually. In that regard, ML techniques, even limited by system constraints, can exhibit significant performance advantages as compared to so-called “sub-optimal” ZF techniques, particularly for flat fading channels and lower order modulation schemes such as Quadrature Amplitude Modulation (QAM)-16 and Quaternary Phase Shift Keying (QPSK) operating at low SNRs.
In theory, under ideal conditions (i.e., no radio frequency (RF) or other system impairments), and where processing power is unlimited, it is expected that exhaustive search ML strategies will always provide superior results as compared to ZF strategies, particularly with respect to MIMO systems. However, in practical applications, RF impairments such as phase noise, flicker noise, power amplifier non-linearities, in-phase/quadrature-phase (I/Q) mismatches, and carrier frequency offsets, either individually or in combination, can affect performance of both ML and ZF techniques.
Therefore, it may be desirable in some instances to provide an equalization system and method that can advantageously employ different data detection techniques depending, for example, upon certain circumstances or certain operating conditions.